Cardiac electrical activity on the surface electrocardiogram reflects myocardial depolarization and conduction (PR and QRS intervals), and repolarization (QT interval). Abnormalities in cardiac conduction and repolarization can lead to cardiac arrhythmias and adverse outcomes (atrial fibrillation and sudden cardiac death) and can necessitate the placement of cardiac pacemakers and / or defibrillators. Identifying the molecular determinants of cardiac electrical activity will provide insight into arrhythmia generation and potentially help target the development of novel therapies. The allelic architecture of cardiac electrical activity and arrhythmias is currently unknown, but is likely to involve both common variants with modest effects and an aggregation of rare variants with stronger effects. While variation in regulatory regions may have a role in arrhythmias, it is clear that variants in exonic regions that change the molecular structure of a protein can lead to alteration in protein function and influence downstream phenotypes. Indeed, coding region mutations can lead to Mendelian forms of cardiac electrical abnormalities that then lead to clinical arrhythmias (e.g. long and short QT syndromes, progressive cardiac conduction disease). We therefore hypothesize that biologically functional coding region genetic variation will be associated with cardiac electrical activity and arrhythmia in the general population. First we propose to systematically investigate the association of cardiac electrical activity with geneti variation in coding regions in ~45,000 individuals from population-based studies. Second, we will evaluate the clinical relevance of the identified variants by examining their association with atrial fibrillation and sudden cardiac death. Finally, we will assess the biologic mechanism of the identified genes and functionally dissect the role of the variants identified using mouse and zebrafish models. Our ultimate goal is to identify genes and genetic variation that are clinically relevant and biologically functional, and therefore potentially the target of new therapies. This application represents a multi-center collaborative effort to efficiently combine expertise in clinical and molecular cardiology, genomics and statistical genetics, cardiovascular and genetic epidemiology, systems biology and bioinformatics, and animal model functional studies. We leverage existing phenotype data from eight population-based studies of cardiovascular diseases with exome-wide genotyping data from the Exome chip available in these studies, in order to efficiently and cost-effectively examine association of functional coding region variants with variation in cardiac electrical activity and risk of arrhythmias. Importantly, we then translae these genetic associations into functional studies, to understand the role in cardiac electrophysiology and arrhythmias played by the genes and variants identified.